BIOMEDICAL TECHNOLOGY

Course Dates: 22 to 26 June 2026

Course Venue: School of Telecommunications Engineering

Contact Hours: 20

Course Directors:  María Jesús Ledesma & Patricia Sánchez

PART I: Code & Create: Your Gateway to Medical App Development

This two-day workshop will guide you through the process of developing mobile apps designed specifically for biomedical engineering. You'll focus on app usability, data security, and innovation in healthcare technology, empowering you to create solutions that improve patient outcomes.

Students will learn the basic skills and understanding necessary to design and develop simple mobile applications that integrate biomedical data, while focusing on usability, data security, and healthcare-related problem-solving.

PART II: Biomedical Engineering

Biomedical technology deals with innovation in biomedical technologies, especially trends in the development of new devices or solutions that solve specific unmet needs. This course offers an introduction to the current trends on biomedical technologies and the innovation process to solve unmet clinical needs.

Key course takeaways:

• Get acquainted with simple biomedical data integration in mobile apps, fundamental UI/UX design principles to create user-friendly healthcare apps.
• Understand the importance of data privacy and security in biomedical app development.
• Learn how to solve practical problems using mobile technology in the context of healthcare.
• Understand the importance of unmet clinical needs to address impactful problems and develop innovative health technology.
• Learn the main aspects that need to be considered in designing and development of medical devices.
• Learn the basic mobile app development concepts for biomedical engineering applications.

Student profile:

This course is aimed at bachelor-level students.

Registration deadlines:

• Regular registration: 30 April 2026

INTRODUCTION TO BLOCKCHAIN: CRYPTOGRAPHY, TECHNOLOGY FOUNDATIONS AND HANDS-ON DEVELOPMENT

Course Dates: 22 June to 3 July 2026, 10:30 to 13:30

Course Venue: School of Telecommunications Systems Engineering 

Contact Hours: 30

Course Director: Jesús Rodríguez-Molina

This 30-hour course offers a rigorous and practical introduction to blockchain technology. Students learn the fundamental cryptographic mechanisms (hash functions, Merkle trees, digital signatures, consensus algorithms, etc.) and the overall internal structure and operation of blockchain systems. They implement their own Proof-of-Work blockchain from scratch, interact with the Ethereum testnet, and complete a group project applying blockchain concepts to real-world scenarios (traceability, logistics, certification, transparency). The course combines theory, hands-on development, and teamwork, culminating in a final project presentation.

Key course takeaways:

• Knowledge of:
  • Hash functions, Merkle trees, digital signatures and consensus algorithms, along with the main features of blockchain implementations.
  • Blockchain architecture (blocks, headers, linking, mining).
  • Blockchain-based accounts, transactions, gas, and smart contracts.
• Skills:
  • Implement a PoW blockchain.
  • Build and verify Merkle proofs.
  • Develop, deploy and interact with smart contracts.
  • Design a small blockchain-based solution and justify technical choices.
• Competences
  • Work collaboratively in small technical teams.
  • Analyze real-world problems and model blockchain-based solutions.
  • Present and defend a prototype.

Student profile:

• This course is designed for undergraduate and graduate students in computer science, telecommunications, data engineering, or related STEM fields who wish to gain a technical understanding of blockchain systems. 
• Participants should be comfortable with programming in Python, usage of VMWare-based Virtual Machines and familiar with fundamental computing concepts such as algorithms, data structures, and network communication. 
• No prior knowledge of blockchain, cryptography, or distributed systems is required. 
• The course welcomes motivated learners interested in decentralized technologies, digital trust, cybersecurity, or fintech innovation. A proactive attitude, willingness to experiment with code, and ability to work collaboratively in teams will ensure successful participation.

Requirements:

Students are required to bring their own laptop capable of running VMWare-based virtual machines.

Registration deadlines:

• Registration: 30 April 2026

AI-DRIVEN DESIGN: CRAFTING SOLUTIONS THAT PUT USERS FIRST

Course Dates: 29 June to 3 July 2026

Course Venue: School of Computer Systems Engineering (except for walk & talk session which will depart from the School of Industrial Engineering and Design)

Contact Hours: 28

Course Directors: Patricia Abril Jiménez & Alberto Díaz

This course aims to provide a holistic understanding of artificial intelligence (AI) in design, incorporating social innovation, entrepreneurship, and ethical considerations. Aiming to explore the integration of AI in creative processes with technical rigor and critical thinking.

The course also covers the basics of AI development, theoretical concepts, methods, and how AI is practically applied in design. It also includes essential information on the ethical considerations of using AI to design artistic and social innovation processes. The course is designed for individuals with a keen interest in design and a desire to explore the integration of AI in creative processes.

Participants will have recourse to and understand AI techniques crucial for design, such as generative design and natural language processing. Additionally, it explores the latest trends and technologies in the intersection of AI and design.

Key course takeaways:

• Comprehension of fundamental AI concepts in design
• Understanding of AI within a design framework
• Comprehension of how AI is practically used in design through hands-on, digital tasks and experiments
• Critical appraisal of the ethical and aesthetic implications of AI in design, considering various situations, environments, and outcomes
• Understanding entrepreneurship and social innovation in the context of AI design

Student profile:

This course is aimed at bachelor-level students of mainly design and computer science. This course equips students with practical and theoretical tools to excel in the dynamic space where design and technology converge. The ideal student profile includes:

• Design enthusiasts: Students with a background or interest in design, including but not limited to graphic design, industrial design, user experience (UX) design, and other creative disciplines
• Technology and AI enthusiasts: Individuals with a curiosity about AI and its applications in various fields, who want to explore how AI can enhance and transform design practices
•  Students of Arts and Humanities: Individuals studying or interested in arts, humanities, and related fields, seeking to bridge the gap between creativity and technology through AI applications in design

Registration deadlines:

• Registration: 30 April 2026

INTRODUCTION TO WIRELESS SENSOR NETWORKS AND IoT SYSTEMS

Course Dates: 6 to 10 July 2025, 10:30 to 14:30

Course Venue: School of TelecommunicationsSystems Engineering 

Contact Hours: 20

Course Director: Jesús Rodríguez-Molina

This 20-hour course introduces students to the architecture, protocols, and practical implementation of Wireless Sensor Networks (WSNs) within modern Internet of Things (IoT) systems. Students learn sensing principles, wireless communication technologies, protocols, data modelling, and the challenges of resource-constrained distributed systems. Through hands-on exercises, participants will deploy physical sensor nodes, analyze measurement data, design a small IoT monitoring application, and understand integration with cloud/edge platforms. The course is a blend of theory and team-based prototyping.  

Key course takeaways:

• Understanding of the core principles of Wireless Sensor Networks (WSNs), including sensing mechanisms, node architecture, wireless communication constraints, and distributed system behavior
•  Explanation of the operation of low-power wireless technologies such as IEEE 802.15.4, BLE, WiFi low-power modes, etc. and analysis of their suitability for different application scenarios.
• Description and comparison of routing protocols used in WSNs (flooding, gossiping, clustering, geographic routing), assessing their energy consumption, scalability, and reliability.
• Modeling and processing of sensor data flows, from raw measurements to metadata structures, using modern IoT standards such as JSON, MQTT, and CoAP.
• Deployment of a complete miniature IoT pipeline, collecting sensor-like data, transmitting it to a gateway or broker, and visualizing it through an edge or cloud dashboard.
• Analysis of energy constraints, network topology impacts, and interoperability needs in order to design functional and efficient WSN solutions.
• Application of WSN concepts to a real-world scenario, designing and presenting a small prototype monitoring system through group work.
• Use of professional tools and development environments (MQTT brokers, Node-RED, development SDKs) to implement and validate distributed sensing applications.

Student profile:

• Undergraduate or graduate students in engineering, computer science, telecommunications, electronics, IoT, or related fields
• Basic programming experience, sufficient to write simple scripts or understand code examples.
• Fundamental understanding of computer networks (IP addressing, basic protocols) is helpful but not mandatory.
• Interest in Internet of Things, embedded systems, wireless communication, distributed sensing, or smart environments.
• No prior experience with Wireless Sensor Networks is required; all essential concepts will be introduced from first principles.
• Ability to work collaboratively in small teams during hands-on exercises and the final mini-project.

Requirements:

Students are required to bring their own laptop capable of running VMWare-based virtual machines.

Registration deadlines:

• Registration: 30 April 2026

CLEAN ENERGY

Course Dates: 6 to 10 July 2026

Course venue: School of Telecommunications Engineering & School of Industrial Engineering

Course Directors: David Fuertes & Alberto Abánades

Contact Hours: 20

PART I: Photovoltaic Solar Energy

This course offers a review of the fundamentals, technologies and applications of photovoltaic solar energy.

PART II: Towards the implementation of Hydrogen technologies

This course will provide insights about the state-of-the-art and development of the full value chain of the hydrogen economy from hydrogen generation to hydrogen off-takers.

Key course takeaways:

• Capacity to analyse information related to energy technologies
• Acquisition of an overview of the current energy system and its environmental impacts
• Knowledge of photovoltaic solar energy, its fundamentals, applications and prospects
• Knowledge of the state-of-the art of the hydrogen economy, transport and distribution, integration storage and implementation costs
• Understanding of the critical path for a future role of hydrogen as an energy vector in the future

Student profile:

This course is aimed at bachelor-level science or engineering students with basic knowlege of electronics, physicas and energy, thermodynamics and fluid mechanics .

Registration deadlines:

• Registration: 30 April 2026

AUTONOMOUS VEHICLES

Course Dates: 13 to 17 July 2026

Course Venue: School of Industrial Engineering (ETSII-UPM) & INSIA (Institute of Automobile Research)

Contact Hours: 20

Course Directors: Francisco José Anguita & Felipe Jiménez

PART I:

DRONES

The civil applications of unmanned aerial vehicles (UAV) (also known as remotely piloted systems RPAS or commonly drones) are increasing. This short course mainly focuses on so-called multirotor drones. The two main challenges at present are endurance dependency on the electrical power supply (i.e. batteries) and autonomous maneuvering in relation to the dynamic, changing environment.  This course provides a general overview of the different technologies involved in the execution of autonomous missions with drones, including the study of the flight system itself, estimation of positioning relative to the environment, and the control of these for maneuvering, as well as the definition and execution of complex high-level missions. Various industrial applications carried out by the Computer Vision and Aerial Robotics Research Group at the Universidad Politécnica de Madrid will also be presented, along with the current scientific and technological challenges faced by the international scientific community.

PART II:

Cooperative, Connected and Autonomous Driving

The purpose of this course is to provide an overview of the state of the art and the key enabling technologies in the field of cooperative, connected and autonomous vehicles, such as positioning systems, environment perception and inter-vehicle communication.

Key course takeaways:

• Knowledge of drone flight capablity, components, working principles and challenges
• Knowledge of drone autonomy, components, working principles and challenges
• Knowledge of multiple civil applications of drones
• Drone programming experience
• Lab visit and live real-drone operations
• Understanding of the technological foundations of autonomous, connected and cooperative driving.
• Understanding of the key effects, challenges and experiences of autonomous and connected vehicles

Student profile:

This course is aimed at bachelor-level students with basic knowledge of physics and algebra (first-year undergraduate).

Registration deadlines:

• Registration: 30 April 2026

DATA & SECURITY

Course Dates: 20 July to 24 July 2026

Course Venue: School of Telecommunications Engineering

Contact Hours: 20

Course Directors: Lucía Prieto Santamaria & Xavier Larriva-Novo

PART I: Cybersecurity

Cybersecurity is about balancing risks and opportunities in cyberspace, the global interconnection of digital networks. Cybersecurity is designed to protect digital property, personal information, and business transactions from accidents, errors, and deliberate attacks. The course introduces students to the cybersecurity field, its uses and raison d'être.

PART II:  Data Analysis

An introductory course for engineering students covering the fundamentals of Data Science and AI, from the data value chain to the main problem types and modern models. It uses practical engineering examples and introduces responsible use of Deep Learning and LLMs.

Key course takeaways:

• Introduction to the major topics of the four cybersecurity areas
• Deeper understanding of cybersecurity areas: cybersecurity governance, cybersecurity threats and cybersecurity technologies
• Ability to frame real-world engineering tasks as data/AI problems
• Understanding of the end-to-end flow from data to models to decisions and core concepts in ML, Deep Learning, and LLMs
• Recognition of basic good practices (data quality, validation, risks, and responsible use)

Student profile:

This course is aimed at bachelor-level information and communication technology students.

Registration deadlines:

• Registration: 30 April 2026

ARTIFICIAL INTELLIGENCE

Course Dates: 20 to 24 July 2026

Course Venue: School of Civil Engineering (Retiro Building)

Contact Hours: 20

Course Directors: María Póveda & Víctor Rodríguez

PART I: Introduction to Artificial Intelligence

Artificial Intelligence (AI) refers a kind of intelligence that enables machines to emulate intelligent behavior or to do things that people mostly do better. AI technologies have an important impact on our daily activities in business, as individuals, and as a society. During this course, apart from learning about AI fundamentals and applications, you will use specific techniques about different areas of AI such as knowledge representation and machine learning.

PART II: Deep Learning and Generative

Deep learning, a cutting-edge subset of artificial intelligence, is increasingly pivotal in driving technological and scientific advancements across various sectors. This comprehensive course on the fundamentals of deep learning is thoughtfully designed to guide students through the core concepts and applications of this transformative technology. It begins with an introductory module that lays a solid foundation in the basics of deep learning and artificial neural networks. The course then progresses into the practical realms of computer vision, demonstrating how deep learning techniques are revolutionizing image analysis and interpretation. In the natural language processing (NLP) module, students will explore the ways deep learning is used for understanding and generating human language, a critical aspect of AI's interaction with people in products such as ChatGPT. The course also includes a focus large language models (LLM), where we delve into how to create applications based on this technology. Each theoretical component is complemented by hands-on activities, ensuring a balanced approach that combines conceptual knowledge with practical skills, fostering both understanding and application in this rapidly evolving field.

Key course takeaways:

• Understanding of concepts, foundations, methodologies and technologies of artificial intelligence, machine learning and the semantic web and linked data, open data, data representation languages and web ontologies

• Overview of practical applications of AI technology in the real world

• Solid grasp of the basic concepts, theories, and historical context of deep learning, understanding its role as a key driver in modern artificial intelligence

• Ability to apply deep learning concepts to real-world tasks, gaining practical experience in various domains such as computer vision, natural language processing, and social network analysis

• Analytical skills, enabling participants to critically assess and solve complex problems using deep learning techniques, fostering a mindset for innovation and solution-oriented thinking

• Hands-on experience with the tools and frameworks commonly used in deep learning, preparing participants to effectively implement and manage deep learning projects

• Knowledge and skills necessary for advanced study in the field or to enhance their career prospects in industries where deep learning is relevant

• Preparation to adapt to and embrace emerging trends and innovations in deep learning, ensuring they remain current and relevant in this rapidly evolving field

Student profile:

This course is aimed at bachelor-level students.

Registration deadlines:

• Registration: 30 April 2026